1. Field of the Invention
Embodiments of the present invention generally relate to linear programming (LP) models developed from a non-linear reference tool. More particularly, the invention relates to linear programming (LP) models of a manufacturing facility developed from a thermodynamically-based reference tool.
2. Description of the Related Art
Steady-state, fundamental-principles models have been used to represent a manufacturing facility, which typically consists of a plurality of separate process units or sections of process units that function together to achieve an overall objective of the facility. Difficulties arise in effectively optimizing the operation of a manufacturing facility due to various factors. Such factors can include a vast variety of separate process units and equipment that are contained in the facility. Other factors typically include the large number of process variables, the large number of potential feedstocks and feedstock compositions, operating variables (e.g., flow rates, temperatures, pressures, etc.), product specifications, market constraints and prices (e.g., for feeds, products, and utilities), mechanical constraints, transportation and storage constraints, and weather conditions.
Consequently, manufacturers typically use commercially available, computer based models that have been developed to accurately simulate and/or optimize the operation of their facilities. These commercially available tools are usually one of two types: first principles reference tools or derived tools.
First principles reference tools are based on first principles (i.e., mathematical relationships or logic that utilize accepted scientific theories or laws, such as those regarding chemical thermodynamics and/or kinetics, which theories or laws have been validated through repeated experimental tests) and that typically possess the capability to separately model many or all of the individual process units in a manufacturing facility. First principles reference tools typically contain a library that provides thermodynamic information about how different molecules, components, or pseudo-components will perform in these process units. These tools can be used to create a model of a manufacturing facility, or section thereof, by using the thermodynamic library to individually model the various process units in the facility and then connect the process units appropriately to reflect the overall facility. Such a model can then directly provide heat and material balance information, which can be used for design, equipment rating, equipment performance, simulation, and optimization of the facility.
Unfortunately, first principles reference models tend to be computationally intensive. Accordingly, substantial computer time and resources can be required to run a model based thereon. Examples of commercially available first principles reference tools include HYSIS® and Aspen Plus®, which are products of Aspen Technologies Incorporated of Cambridge, Mass.; PRO/II®, which is a product of SimSci-Esscor, an operating unit of Invensys plc of Cheshire, United Kingdom; and SPYRO®, which is a product of Technip-Coflexip SA of Paris, France.
Recently, a new generation of first principles reference tools has been developed that are capable of modeling, solving, and optimizing an entire manufacturing facility. Examples of these new reference tools are AspenTech RT-OPT®, which is a product of Aspen Technology Incorporated of Cambridge, Mass., and SimSci ROMeo®, which is a product of SimSci-Esscor, an operating unit of Invensys plc, of Cheshire, United Kingdom. These tools are capable of solving very large simulation or optimization problems, usually via a non-linear simultaneous equation solver and/or optimizer. However, given the enormous size and complexity of a first principles reference model for an entire manufacturing facility, as well as its non-linear nature, solution of the model can require huge amounts of computing resources and can take substantial periods of time, especially in optimization mode.
Derived tools, on the other hand, require less computing power and time than a first principles model to solve a problem of similar size and complexity. Derived tools are tools that possess very convenient structures, albeit simplified, to depict many or all of the process unit operations needed to model a manufacturing facility. These derived tools have convenient report writing capabilities, and may possess various analysis tools to help explain the modeling results. In general, derived tools use either linear programming (LP) or sequential linear programming (SLP) type mathematics to solve optimization problems.
However, these tools do not have the capability to model process unit operations based on first principles, nor do they contain a thermodynamic library to describe how different molecules, components, or pseudo-components would perform in such process unit operations. As such, these derived tools cannot directly provide heat and material balance information for use in design, equipment rating, equipment performance, simulation, and optimization of the facility. Rather, a model in these derived tools requires that a depiction of the facility to be modeled be developed in some other engineering tool (e.g., HYSIS®, Aspen Plus®, PRO/II®, and SPYRO®, referred to above, as well as other commercially available engineering tools that would be well known to persons skilled in the art of modeling industrial process facilities). This depiction is then imported into the derived tool.
Nevertheless, given their convenient form and analysis capabilities, as well as the computing advantages of LP or SLP programming, derived tools have generally been preferred for use in operational planning, feedstock selection, and optimization of manufacturing facilities. Examples of commercially available derived tools are AspenTech PIMS®, which is a product of Aspen Technology Incorporated of Cambridge, Mass., and SimSci Petro®, which is a product of SimSci-Esscor, an operating unit of Invensys plc, of Cheshire, United Kingdom.
Due to computing limitations, models based on a combination of first principles reference tools and derived tools have been developed for large processing facilities. Such models typically treat a large processing facility as two or more facilities, where each facility is broken into two or more separate models of individual process units and interconnected to represent the overall facility. By doing so, intermediate stream connectivities have to be accounted for. An intermediate stream is a stream that flows from one process unit into one or more other process units. For example, a product stream from an upstream process unit may become an input stream to one or more downstream process units, or a recycle stream from a downstream process unit may become an input stream to one or more upstream process units. Thus, a change in the products from a particular upstream process unit may cause a change in a recycle stream from a downstream process unit, which in turn may cause another change in the same or a different upstream process unit. The overall derived computer model for the facility must accurately model these effects.
There are several inherent problems with representing a manufacturing facility by two or more separate models. For example, a complex process unit, such as a reactor or steam cracker, may have fifteen or more separate process units that must be accurately modeled to create the overall derived model. Such complex process units may also have a large number of recycle streams that must be accurately modeled. When the individual derived models for each process unit and recycle streams are joined together to form the overall derived model for the facility, inconsistencies between individual derived models (e.g., inconsistencies in the underlying engineering tools or in the heat and material balance basis) can result in a more difficult validation process and, in some situations, in non-convergence or unacceptable inaccuracies in the overall model.
U.S. Patent Application Publication No. 2003/0097243 A1 discloses a computerized system and method for operating a hydrocarbon or chemical production facility, comprising mathematically modeling the facility; optimizing the mathematic model with a combination of linear and non-linear solvers; and generating one or more product recipes based upon the optimized solution. In one embodiment, the mathematic model further comprises a plurality of process equations having process variables and corresponding coefficients. Preferably, these process variables and corresponding coefficients are used to create a matrix in a linear program. The linear program may be executed by recursion or distributed recursion. Upon successive recursion passes, updated values for a portion of the process variables and corresponding coefficients are calculated by the linear solver and by a non-linear solver, and the updated values for the process variables and corresponding coefficients are substituted into the matrix. Unfortunately, the simultaneous use of multiple solvers, some of which are non-linear, can result in significant computing time and resource disadvantages.
U.S. Pat. No. 5,666,297 discloses a software system for simulating and optimizing a processing plant design. The software system includes a plurality of dual mode equipment models for simulating each piece of equipment in the processing plant design. A sequential modular simulation routine is used to execute the equipment models in a first mode to define a first set of values of the operating parameters of the processing plant design. Then, a simultaneous simulation/optimization routine executes the equipment models in a second mode. The simultaneous simulation/optimization routine utilizes the first set of values for the plant's operating parameters from the sequential simulation routine and subsequently determines a second set of values of the operating parameters at which the processing plant design is optimized. The equipment models after execution by the sequential simulation routine and the simultaneous simulation/optimization routine store the first and second sets of values for the operating parameters in a common plant model file.
U.S. Pat. No. 6,442,513 discloses a method for real-time optimization of an oil refinery, or a portion thereof, where a fluid stream having multiple physical components is modeled as a plurality of pseudo-components. Each physical component has a boiling point, and each pseudo-component has a pre-defined boiling point and includes all physical components from the fluid stream having approximately the pre-defined boiling point. According to this patent, good modeling results may be obtained by grouping compounds and molecules into pseudo-components or lumps based on boiling points, and by modeling based on such lumps. This is especially true in view of the fact that much of the operation of a refinery depends on boiling points of compositional components of crude oil.
U.S. Pat. No. 6,721,610 discloses a method for pre-calculating the parameters of industrial processes and/or products. According to this method, a vector of admissible input variables of the industrial process and/or product is defined. Definition ranges are assigned to each variable in the input vector. A process output vector is determined with the pre-calculable process parameters. Known information on the process is stored in a data bank and ranges of validity for the process input variables are allocated to this information. For each process input vector inputted from an admissible definition range provided with valid information, exactly one process output vector is determined according to the information.
U.S. Pat. No. 7,257,451 discloses a method for creating a LP model of an industrial process facility from a first principles reference tool to interactively simulate and/or optimize the operation of the facility to facilitate or optimize feedstock selection and/or other economic analyses based on varying prices, availabilities, and other external constraints.
However, none of these models describe how to simulate the impact of partially withdrawing intermediate streams from within the process facility to simulate the impact on products and facilities. There is a need, therefore, for an improved method for interactively simulating and/or optimizing the operation of a facility to facilitate or optimize feedstock selection and/or other economic analyses based on varying prices, availabilities, and other external constraints.